DOCSLIB.ORG

Global Cyclone and Anticyclone Detection Model Based on Remotely Sensed Wind Field and Deep Learning

Total Page：16

File Type：pdf, Size：1020Kb

Download full-text PDF Read full-text
Global Cyclone and Anticyclone Detection Model Based on Remotely Sensed Wind Field and Deep Learning remote sensing Letter Global Cyclone and Anticyclone Detection Model Based on Remotely Sensed Wind Field and Deep Learning Ming Xie , Ying Li * and Kai Cao Navigation College, Dalian Maritime University, Dalian 116026, China; [email protected] (M.X.); [email protected] (K.C.) * Correspondence: [email protected] Received: 8 September 2020; Accepted: 21 September 2020; Published: 23 September 2020 Abstract: Cyclone detection is a classical topic and researchers have developed various methods of cyclone detection based on sea-level pressure, cloud image, wind field, etc. In this article, a deep-learning algorithm is incorporated with modern remote-sensing technology and forms a global-scale cyclone/anticyclone detection model. Instead of using optical images, wind field data obtained from Mean Wind Field-Advanced Scatterometer (MWF-ASCAT) is utilized as the dataset for model training and testing. The wind field vectors are reconstructed and fed to the deep-learning model, which is built based on a faster-region with convolutional neural network (faster-RCNN). The model consists of three modules: a series of convolutional and pooling layers as the feature extractor, a region proposal network that searches for the potential areas of cyclone/anticyclone within the dataset, and the classifier that classifies the proposed region as cyclone or anticyclone through a fully-connected neural network. Compared with existing methods of cyclone detection, the test results indicate that this model based on deep learning is able to reduce the number of false alarms, and at the same time, maintain high accuracy in cyclone detection. An application of this method is presented in the article. By processing temporally continuous data of wind field, the model is able to track the path of Hurricane Irma in September, 2017. The advantages and limitations of the model are also discussed in the article. Keywords: cyclone detection; remote sensing; wind field; deep learning 1. Introduction Cyclone detection is a classical yet still actively developing topic, since it provides the theoretical foundation for the responses of extreme weather condition, as well as studies of the Earth’s climate system. As the development of modern (especially satellite-based) remote sensing, detecting cyclones in the larger temporal and spatial scale has become a trend of relevant studies [1]. Traditional cyclone detection methods focus on the characteristics of a pressure gradient, and detect the cyclones by locating the low-pressure centers [2–4]. Rudeva and Gulev analyzed sea-level pressure (SLP) data over the northern hemisphere, based on which they detected cyclones by locating the minimum points of SLP [5]. Based on this method, Simmonds et al. proposed an automatic cyclone detection algorithm by comparing the Laplacian operator of SLP at each grid point to those at neighboring grid points [6]. Hanley and Caballero also developed an identification and tracking method that specifically recognized multicenter cyclones by examining the gradient of SLP and finding minimum points [7]. Although the minimum SLP method can effectively detect an ideal cyclone, the accuracy of the detection result is limited due to the lack of in situ data and ambiguities in satellite presentation. Remote Sens. 2020, 12, 3111; doi:10.3390/rs12193111 www.mdpi.com/journal/remotesensing Remote Sens. 2020, 12, 3111 2 of 13 Because the wind field of a cyclone has strong characteristics in geometric symmetry, the vector field formed by the wind speed certainly has the potential for detecting cyclones. Fluid dynamicists developed various methods of identifying a vortex core in the fluid field [8,9]. The Okubo–Weiss (OW) approach is one of the most well-known methods of distinguishing high vorticity zone within the flow field by calculating OW parameter using strain and vorticity of the velocity field as follows [10,11]: W = S2 + S2 !2 (1) s n − where Ss and Sn are the shear and normal components of strain, and ! is the relative vorticity. They can be calculated using the following equations: @V0 @U0 @U0 @V0 @V0 @U0 Ss = + , Sn = , ! = (2) @x @y @x − @y @x − @y where U’ and V’ are the zonal and meridional components of the flow velocity; x and y represent the coordinates. The OW parameter quantitatively describes the significance of rotation compared with deformation in the flow field. Generally, the vortex core is characterized as strong rotation, and has an OW parameter of negative value; while vortex ring is characterized as strong deformation, and has an OW parameter of positive value. The OW parameter that is close to 0 indicates the background with no vortex-shape events. By defining a threshold value W0, vortex core can be identified by searching for the areas with OW parameter that is smaller than the predefined threshold. This method has been widely used in oceanography and meteorology. For example, numerous studies have applied the OW approach on sea-level anomaly (SLA) data and developed identification models for oceanic eddies [12–18]. Similarly, a cyclone could be detected by examining the vorticity of the wind field, since the cyclone eyes usually have high vorticity [19]. Wong et al. studied the patterns of a spiraling vector field and developed a method of identifying a circulating center. They suggested that this method could potentially be applied to detect cyclone eyes [20]. Following this method, Inatsu proposed a cyclone identification method by comparing the vorticity field of the cyclone eyes with that of the neighborhoods [21]. With the data collected by modern remote-sensing techniques, researchers are able to identify cyclones in larger spatial scale. Zou et al. proposed an automatic cyclone detection algorithm using the wind vector obtained from the Quick Scatterometter (QuikSCAT) satellite [22]. Warunsin and Chitsobhuk further combined the wind data with cloud shape and developed a fuzzy inference system for cyclone detection [23]. However, such studies usually focus on a regional area, even with the data obtained at global scale [14–17]. This is partly because of the difficulties in determining the appropriate threshold W0 for the OW parameter at the global scale. In practice, the threshold W0 needs to be adjusted for different location and time when identifying vortex cores in large-scale [24] or time series [25] data. Other approaches are proposed by researchers in order to avoid using the regional threshold and overcome this limitation. For examples, Williams et al. simulated the physics and geometry of idealized vortex, and presented the R2 method, which determines how well an eddy confirms to the characteristics of an ideal Gaussian vortex through linear regression [26]. Peterson et al. conducted a large-scale simulation on oceanic eddies, and identified the simulated eddies using the R2 method [27]. Li et al. proposed an iterative algorithm for eddy detection, in which the eddy regions are separated from non-eddy regions through data segmentation, and then recognized using a density clustering method [28]. As in the development of artificial intelligence (AI) technology in the last decade, machine learning algorithm has been successfully applied in the subjects of object detection and pattern recognition owing to its ability to efficiently extract imagery information. In the subjects of oceanography and meteorology, state-of-the-art machine-learning algorithms have been applied to recognize the circulating pattern in a spiraling fluid field [29]. Moreover, deep convolutional neural network (DCNN) has been utilized to detect and classify objects in remote-sensing images [30,31]. Based on DCNN, Remote Sens. 2020, 12, 3111 3 of 13 researchers have successfully developed object detection models for land-use and land-cover [32,33], vegetation [34,35], urban commerce [36,37], transportation vehicles [38,39], etc. Similarly, attempts are made to detect cyclones based on cloud images. Lee and Liu classified the appearance of tropical cyclones in remote-sensing images into eight possible categories, and then trained the neural network based on this classification [40]. The drawback of using remote-sensing images as the training dataset of cyclone detection is that the appearance of some non-cyclone clouds is similar to those of cyclones. That fact negatively affects the accuracy of the identification model [41,42]. Bai et al., constructed and trained a deep-learning model for oceanic eddy detection using enhanced maps of a streampath, which are produced using ocean velocity field data [43]. Ho and Talukder conducted a series of studies and proposed automatic cyclone identification methods based on a support vector machine (SVM) and dominant wind direction (DOWD) classifier using the wind data from the QuikSCAT satellite [1,44,45]. They further combined the wind data from QuikSCAT and precipitation data from the Tropical Rainfall Measurement Mission (TRMM), and built a Cyclone Discovery Module (CDM) that was able to achieve accurate detection results [1,45]. These successful applications of machine learning illustrate its ability of analyzing complex systems that are not well understood, but with plenty of observations. With the extensive data collected by modern remote-sensing technology, obviously, the machine-learning method could be potentially applied to study the complex climate system, including cyclone and anticyclone. In this study, a cross-boundary research is achieved by applying a state-of-the-art deep learning model into a classical research field. The wind field vector in the global scale is reconstructed to form the training and testing dataset for the deep-learning model, and the model is modified in order to work with wind field data. The deep-learning technology is expected to accomplish a large-scale cyclone/anticyclone detection that is difficult to achieve with traditional methods. 2. Methodology 2.1. Dataset Acquisition and Reconstruction Instead of using optical remote-sensing images, wind speed data is utilized to construct the dataset. Specifically, mean wind fields (MWF) data obtained by the Advanced Scatterometer (ASCAT) is applied in this study.
Load more

Recommended publications
  • P1.24 a Typhoon Loss Estimation Model for China
    P1.24 A TYPHOON LOSS ESTIMATION MODEL FOR CHINA Peter J. Sousounis*, H. He, M. L. Healy, V. K. Jain, G. Ljung, Y. Qu, and B. Shen-Tu AIR Worldwide Corporation, Boston, MA 1. INTRODUCTION the two. Because of its wind intensity (135 mph maximum sustained winds), it has been Nowhere 1 else in the world do tropical compared to Hurricane Katrina 2005. But Saomai cyclones (TCs) develop more frequently than in was short lived, and although it made landfall as the Northwest Pacific Basin. Nearly thirty TCs are a strong Category 4 storm and generated heavy spawned each year, 20 of which reach hurricane precipitation, it weakened quickly. Still, economic or typhoon status (cf. Fig. 1). Five of these reach losses were ~12 B RMB (~1.5 B USD). In super typhoon status, with windspeeds over 130 contrast, Bilis, which made landfall a month kts. In contrast, the North Atlantic typically earlier just south of where Saomai hit, was generates only ten TCs, seven of which reach actually only tropical storm strength at landfall hurricane status. with max sustained winds of 70 mph. Bilis weakened further still upon landfall but turned Additionally, there is no other country in the southwest and traveled slowly over a period of world where TCs strike with more frequency than five days across Hunan, Guangdong, Guangxi in China. Nearly ten landfalling TCs occur in a and Yunnan Provinces. It generated copious typical year, with one to two additional by-passing amounts of precipitation, with large areas storms coming close enough to the coast to receiving more than 300 mm.
    [Show full text]
  • A Study of Synoptic-Scale Tornado Regimes
    Garner, J. M., 2013: A study of synoptic-scale tornado regimes. Electronic J. Severe Storms Meteor., 8 (3), 1–25. A Study of Synoptic-Scale Tornado Regimes JONATHAN M. GARNER NOAA/NWS/Storm Prediction Center, Norman, OK (Submitted 21 November 2012; in final form 06 August 2013) ABSTRACT The significant tornado parameter (STP) has been used by severe-thunderstorm forecasters since 2003 to identify environments favoring development of strong to violent tornadoes. The STP and its individual components of mixed-layer (ML) CAPE, 0–6-km bulk wind difference (BWD), 0–1-km storm-relative helicity (SRH), and ML lifted condensation level (LCL) have been calculated here using archived surface objective analysis data, and then examined during the period 2003−2010 over the central and eastern United States. These components then were compared and contrasted in order to distinguish between environmental characteristics analyzed for three different synoptic-cyclone regimes that produced significantly tornadic supercells: cold fronts, warm fronts, and drylines. Results show that MLCAPE contributes strongly to the dryline significant-tornado environment, while it was less pronounced in cold- frontal significant-tornado regimes. The 0–6-km BWD was found to contribute equally to all three significant tornado regimes, while 0–1-km SRH more strongly contributed to the cold-frontal significant- tornado environment than for the warm-frontal and dryline regimes. –––––––––––––––––––––––– 1. Background and motivation As detailed in Hobbs et al. (1996), synoptic- scale cyclones that foster tornado development Parameter-based and pattern-recognition evolve with time as they emerge over the central forecast techniques have been essential and eastern contiguous United States (hereafter, components of anticipating tornadoes in the CONUS).
    [Show full text]
  • Soaring Weather
    Chapter 16 SOARING WEATHER While horse racing may be the "Sport of Kings," of the craft depends on the weather and the skill soaring may be considered the "King of Sports." of the pilot. Forward thrust comes from gliding Soaring bears the relationship to flying that sailing downward relative to the air the same as thrust bears to power boating. Soaring has made notable is developed in a power-off glide by a conven­ contributions to meteorology. For example, soar­ tional aircraft. Therefore, to gain or maintain ing pilots have probed thunderstorms and moun­ altitude, the soaring pilot must rely on upward tain waves with findings that have made flying motion of the air. safer for all pilots. However, soaring is primarily To a sailplane pilot, "lift" means the rate of recreational. climb he can achieve in an up-current, while "sink" A sailplane must have auxiliary power to be­ denotes his rate of descent in a downdraft or in come airborne such as a winch, a ground tow, or neutral air. "Zero sink" means that upward cur­ a tow by a powered aircraft. Once the sailcraft is rents are just strong enough to enable him to hold airborne and the tow cable released, performance altitude but not to climb. Sailplanes are highly 171 r efficient machines; a sink rate of a mere 2 feet per second. There is no point in trying to soar until second provides an airspeed of about 40 knots, and weather conditions favor vertical speeds greater a sink rate of 6 feet per second gives an airspeed than the minimum sink rate of the aircraft.
    [Show full text]
  • Lecture 15 Hurricane Structure
    MET 200 Lecture 15 Hurricanes Last Lecture: Atmospheric Optics Structure and Climatology The amazing variety of optical phenomena observed in the atmosphere can be explained by four physical mechanisms. • What is the structure or anatomy of a hurricane? • How to build a hurricane? - hurricane energy • Hurricane climatology - when and where Hurricane Katrina • Scattering • Reflection • Refraction • Diffraction 1 2 Colorado Flood Damage Hurricanes: Useful Websites http://www.wunderground.com/hurricane/ http://www.nrlmry.navy.mil/tc_pages/tc_home.html http://tropic.ssec.wisc.edu http://www.nhc.noaa.gov Hurricane Alberto Hurricanes are much broader than they are tall. 3 4 Hurricane Raymond Hurricane Raymond 5 6 Hurricane Raymond Hurricane Raymond 7 8 Hurricane Raymond: wind shear Typhoon Francisco 9 10 Typhoon Francisco Typhoon Francisco 11 12 Typhoon Francisco Typhoon Francisco 13 14 Typhoon Lekima Typhoon Lekima 15 16 Typhoon Lekima Hurricane Priscilla 17 18 Hurricane Priscilla Hurricanes are Tropical Cyclones Hurricanes are a member of a family of cyclones called Tropical Cyclones. West of the dateline these storms are called Typhoons. In India and Australia they are called simply Cyclones. 19 20 Hurricane Isaac: August 2012 Characteristics of Tropical Cyclones • Low pressure systems that don’t have fronts • Cyclonic winds (counter clockwise in Northern Hemisphere) • Anticyclonic outflow (clockwise in NH) at upper levels • Warm at their center or core • Wind speeds decrease with height • Symmetric structure about clear "eye" • Latent heat from condensation in clouds primary energy source • Form over warm tropical and subtropical oceans NASA VIIRS Day-Night Band 21 22 • Differences between hurricanes and midlatitude storms: Differences between hurricanes and midlatitude storms: – energy source (latent heat vs temperature gradients) - Winter storms have cold and warm fronts (asymmetric).
    [Show full text]
  • Investigation and Prediction of Hurricane Eyewall
    INVESTIGATION AND PREDICTION OF HURRICANE EYEWALL REPLACEMENT CYCLES By Matthew Sitkowski A dissertation submitted in partial fulfillment of the requirements for the degree of Doctor of Philosophy (Atmospheric and Oceanic Sciences) at the UNIVERSITY OF WISCONSIN-MADISON 2012 Date of final oral examination: 4/9/12 The dissertation is approved by the following members of the Final Oral Committee: James P. Kossin, Affiliate Professor, Atmospheric and Oceanic Sciences Daniel J. Vimont, Professor, Atmospheric and Oceanic Sciences Steven A. Ackerman, Professor, Atmospheric and Oceanic Sciences Jonathan E. Martin, Professor, Atmospheric and Oceanic Sciences Gregory J. Tripoli, Professor, Atmospheric and Oceanic Sciences i Abstract Flight-level aircraft data and microwave imagery are analyzed to investigate hurricane secondary eyewall formation and eyewall replacement cycles (ERCs). This work is motivated to provide forecasters with new guidance for predicting and better understanding the impacts of ERCs. A Bayesian probabilistic model that determines the likelihood of secondary eyewall formation and a subsequent ERC is developed. The model is based on environmental and geostationary satellite features. A climatology of secondary eyewall formation is developed; a 13% chance of secondary eyewall formation exists when a hurricane is located over water, and is also utilized by the model. The model has been installed at the National Hurricane Center and has skill in forecasting secondary eyewall formation out to 48 h. Aircraft reconnaissance data from 24 ERCs are examined to develop a climatology of flight-level structure and intensity changes associated with ERCs. Three phases are identified based on the behavior of the maximum intensity of the hurricane: intensification, weakening and reintensification.
    [Show full text]
  • Observed Cyclone–Anticyclone Tropopause Vortex Asymmetries
    JANUARY 2005 H A K I M A N D CANAVAN 231 Observed Cyclone–Anticyclone Tropopause Vortex Asymmetries GREGORY J. HAKIM AND AMELIA K. CANAVAN University of Washington, Seattle, Washington (Manuscript received 30 September 2003, in final form 28 June 2004) ABSTRACT Relatively little is known about coherent vortices near the extratropical tropopause, even with regard to basic facts about their frequency of occurrence, longevity, and structure. This study addresses these issues through an objective census of observed tropopause vortices. The authors test a hypothesis regarding vortex-merger asymmetry where cyclone pairs are repelled and anticyclone pairs are attracted by divergent flow due to frontogenesis. Emphasis is placed on arctic vortices, where jet stream influences are weaker, in order to facilitate comparisons with earlier idealized numerical simulations. Results show that arctic cyclones are more numerous, persistent, and stronger than arctic anticyclones. An average of 15 cyclonic vortices and 11 anticyclonic vortices are observed per month, with maximum frequency of occurrence for cyclones (anticyclones) during winter (summer). There are are about 47% more cyclones than anticyclones that survive at least 4 days, and for longer lifetimes, 1-day survival probabilities are nearly constant at 65% for cyclones, and 55% for anticyclones. Mean tropopause potential-temperature amplitude is 13 K for cyclones and 11 K for anticyclones, with cyclones exhibiting a greater tail toward larger values. An analysis of close-proximity vortex pairs reveals divergence between cyclones and convergence be- tween anticyclones. This result agrees qualitatively with previous idealized numerical simulations, although it is unclear to what extent the divergent circulations regulate vortex asymmetries.
    [Show full text]
  • HS Science Distance Learning Activities
    HS Science (Earth Science/Physics) Distance Learning Activities TULSA PUBLIC SCHOOLS Dear families, These learning packets are filled with grade level activities to keep students engaged in learning at home. We are following the learning routines with language of instruction that students would be engaged in within the classroom setting. We have an amazing diverse language community with over 65 different languages represented across our students and families. If you need assistance in understanding the learning activities or instructions, we recommend using these phone and computer apps listed below. Google Translate • Free language translation app for Android and iPhone • Supports text translations in 103 languages and speech translation (or conversation translations) in 32 languages • Capable of doing camera translation in 38 languages and photo/image translations in 50 languages • Performs translations across apps Microsoft Translator • Free language translation app for iPhone and Android • Supports text translations in 64 languages and speech translation in 21 languages • Supports camera and image translation • Allows translation sharing between apps 3027 SOUTH NEW HAVEN AVENUE | TULSA, OKLAHOMA 74114 918.746.6800 | www.tulsaschools.org TULSA PUBLIC SCHOOLS Queridas familias: Estos paquetes de aprendizaje tienen actividades a nivel de grado para mantener a los estudiantes comprometidos con la educación en casa. Estamos siguiendo las rutinas de aprendizaje con las palabras que se utilizan en el salón de clases. Tenemos una increíble
    [Show full text]
  • Extratropical Cyclones and Anticyclones
    © Jones & Bartlett Learning, LLC. NOT FOR SALE OR DISTRIBUTION Courtesy of Jeff Schmaltz, the MODIS Rapid Response Team at NASA GSFC/NASA Extratropical Cyclones 10 and Anticyclones CHAPTER OUTLINE INTRODUCTION A TIME AND PLACE OF TRAGEDY A LiFE CYCLE OF GROWTH AND DEATH DAY 1: BIRTH OF AN EXTRATROPICAL CYCLONE ■■ Typical Extratropical Cyclone Paths DaY 2: WiTH THE FI TZ ■■ Portrait of the Cyclone as a Young Adult ■■ Cyclones and Fronts: On the Ground ■■ Cyclones and Fronts: In the Sky ■■ Back with the Fitz: A Fateful Course Correction ■■ Cyclones and Jet Streams 298 9781284027372_CH10_0298.indd 298 8/10/13 5:00 PM © Jones & Bartlett Learning, LLC. NOT FOR SALE OR DISTRIBUTION Introduction 299 DaY 3: THE MaTURE CYCLONE ■■ Bittersweet Badge of Adulthood: The Occlusion Process ■■ Hurricane West Wind ■■ One of the Worst . ■■ “Nosedive” DaY 4 (AND BEYOND): DEATH ■■ The Cyclone ■■ The Fitzgerald ■■ The Sailors THE EXTRATROPICAL ANTICYCLONE HIGH PRESSURE, HiGH HEAT: THE DEADLY EUROPEAN HEAT WaVE OF 2003 PUTTING IT ALL TOGETHER ■■ Summary ■■ Key Terms ■■ Review Questions ■■ Observation Activities AFTER COMPLETING THIS CHAPTER, YOU SHOULD BE ABLE TO: • Describe the different life-cycle stages in the Norwegian model of the extratropical cyclone, identifying the stages when the cyclone possesses cold, warm, and occluded fronts and life-threatening conditions • Explain the relationship between a surface cyclone and winds at the jet-stream level and how the two interact to intensify the cyclone • Differentiate between extratropical cyclones and anticyclones in terms of their birthplaces, life cycles, relationships to air masses and jet-stream winds, threats to life and property, and their appearance on satellite images INTRODUCTION What do you see in the diagram to the right: a vase or two faces? This classic psychology experiment exploits our amazing ability to recognize visual patterns.
    [Show full text]
  • ESSENTIALS of METEOROLOGY (7Th Ed.) GLOSSARY
    ESSENTIALS OF METEOROLOGY (7th ed.) GLOSSARY Chapter 1 Aerosols Tiny suspended solid particles (dust, smoke, etc.) or liquid droplets that enter the atmosphere from either natural or human (anthropogenic) sources, such as the burning of fossil fuels. Sulfur-containing fossil fuels, such as coal, produce sulfate aerosols. Air density The ratio of the mass of a substance to the volume occupied by it. Air density is usually expressed as g/cm3 or kg/m3. Also See Density. Air pressure The pressure exerted by the mass of air above a given point, usually expressed in millibars (mb), inches of (atmospheric mercury (Hg) or in hectopascals (hPa). pressure) Atmosphere The envelope of gases that surround a planet and are held to it by the planet's gravitational attraction. The earth's atmosphere is mainly nitrogen and oxygen. Carbon dioxide (CO2) A colorless, odorless gas whose concentration is about 0.039 percent (390 ppm) in a volume of air near sea level. It is a selective absorber of infrared radiation and, consequently, it is important in the earth's atmospheric greenhouse effect. Solid CO2 is called dry ice. Climate The accumulation of daily and seasonal weather events over a long period of time. Front The transition zone between two distinct air masses. Hurricane A tropical cyclone having winds in excess of 64 knots (74 mi/hr). Ionosphere An electrified region of the upper atmosphere where fairly large concentrations of ions and free electrons exist. Lapse rate The rate at which an atmospheric variable (usually temperature) decreases with height. (See Environmental lapse rate.) Mesosphere The atmospheric layer between the stratosphere and the thermosphere.
    [Show full text]
  • Tropical Cyclone Mesoscale Circulation Families
    DOMINANT TROPICAL CYCLONE OUTER RAINBANDS RELATED TO TORNADIC AND NON-TORNADIC MESOSCALE CIRCULATION FAMILIES Scott M. Spratt and David W. Sharp National Weather Service Melbourne, Florida 1. INTRODUCTION Doppler (WSR-88D) radar sampling of Tropical Cyclone (TC) outer rainbands over recent years has revealed a multitude of embedded mesoscale circulations (e.g. Zubrick and Belville 1993, Cammarata et al. 1996, Spratt el al. 1997, Cobb and Stuart 1998). While a majority of the observed circulations exhibited small horizontal and vertical characteristics similar to extra- tropical mini supercells (Burgess et al. 1995, Grant and Prentice 1996), some were more typical of those common to the Great Plains region (Sharp et al. 1997). During the past year, McCaul and Weisman (1998) successfully simulated the observed spectrum of TC circulations through variance of buoyancy and shear parameters. This poster will serve to document mesoscale circulation families associated with six TC's which made landfall within Florida since 1994. While tornadoes were not associated with all of the circulations (manual not algorithm defined), those which exhibited persistent and relatively strong rotation did often correlate with touchdowns (Table 1). Similarities between tornado- producing circulations will be discussed in Section 7. Contained within this document are 0.5 degree base reflectivity and storm relative velocity images from the Melbourne (KMLB; Gordon, Erin, Josephine, Georges), Jacksonville (KJAX; Allison), and Eglin Air Force Base (KEVX; Opal) WSR-88D sites. Arrows on the images indicate cells which produced persistent rotation. 2. TC GORDON (94) MESO CHARACTERISTICS KMLB radar surveillance of TC Gordon revealed two occurrences of mesoscale families (first period not shown).
    [Show full text]
  • Central Region Technical Attachment 95-08 Examination of an Apparent
    CRH SSD APRIL 1995 CENTRAL REGION TECHNICAL ATTACHMENT 95-08 EXAMINATION OF AN APPARENT LANDSPOUT IN THE EASTERN BLACK HILLS OF WESTERN SOUTH DAKOTA David L. Hintz1 and Matthew J. Bunkers National Weather Service Office Rapid City, South Dakota 1. Abstract On June 29, 1994, an apparent landspout occurred in the Black Hills of South Dakota. This landspout exhibited most of the features characteristic of traditional landspouts documented in eastern Colorado. The landspout lasted 3 to 8 minutes, had a width of less than 20 m and a path of 1 to 3 km, produced estimated wind speeds of Fl intensity (33 to 50 m s1), and emanated from a towering cumulus (TCU) cloud located along a quasi-stationary convergencq/cyclonic shear zone. No radar echo was observed with this event; however, a supercell thunderstorm was located 80-100 km to the east. National Weather Service meteorologists surveyed the “very localized” damage area and ruled out the possibility of the landspout being related to microburst, gustnado, or dust devil activity, as winds away from the landspout were less than 3 m s1. The landspout apparently “detached” from the parent TCU and damaged a farm which resulted in $1,000 dollars in expenses. 2. Introduction During the late 1980’s and early 1990’s researchers documented a phe­ nomenon with subtle differences from traditional tornadoes and waterspouts, herein referred to as the landspout (Seargent 1994; Brady and Szoke 1988, 1989; Bluestein 1985). The term “landspout” was actually coined by Bluestein (I985)(in the formal literature) when he observed this type of vortex along an Oklahoma squall line.
    [Show full text]
  • The Interactions Between a Midlatitude Blocking Anticyclone and Synoptic-Scale Cyclones That Occurred During the Summer Season
    502 MONTHLY WEATHER REVIEW VOLUME 126 NOTES AND CORRESPONDENCE The Interactions between a Midlatitude Blocking Anticyclone and Synoptic-Scale Cyclones That Occurred during the Summer Season ANTHONY R. LUPO AND PHILLIP J. SMITH Department of Earth and Atmospheric Sciences, Purdue University, West Lafayette, Indiana 20 September 1996 and 2 May 1997 ABSTRACT Using the Goddard Laboratory for Atmospheres Goddard Earth Observing System 5-yr analyses and the Zwack±Okossi equation as the diagnostic tool, the horizontal distribution of the dynamic and thermodynamic forcing processes contributing to the maintenance of a Northern Hemisphere midlatitude blocking anticyclone that occurred during the summer season were examined. During the development of this blocking anticyclone, vorticity advection, supported by temperature advection, forced 500-hPa height rises at the block center. Vorticity advection and vorticity tilting were also consistent contributors to height rises during the entire life cycle. Boundary layer friction, vertical advection of vorticity, and ageostrophic vorticity tendencies (during decay) consistently opposed block development. Additionally, an analysis of this blocking event also showed that upstream precursor surface cyclones were not only important in block development but in block maintenance as well. In partitioning the basic data ®elds into their planetary-scale (P) and synoptic-scale (S) components, 500-hPa height tendencies forced by processes on each scale, as well as by interactions (I) between each scale, were also calculated. Over the lifetime of this blocking event, the S and P processes were most prominent in the blocked region. During the formation of this block, the I component was the largest and most consistent contributor to height rises at the center point.
    [Show full text]